

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
		raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Any   =     "Hello world! cécé herlolip"






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Optional[Any]      		=					FairseqRobertaModel.from_pretrained(lowerCamelCase_		)
							roberta.eval()  # disable dropout
							A      :					int      		=					roberta.model.encoder.sentence_encoder
							A      :					Tuple      		=					XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,				hidden_size=roberta.cfg.model.encoder_embed_dim     ,				num_hidden_layers=roberta.cfg.model.encoder_layers     ,				num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,				max_position_embeddings=514     ,				type_vocab_size=1     ,				layer_norm_eps=1E-5     ,				)
							if classification_head:
														A      :					Optional[Any]      		=					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our RoBERTa config:'''     ,				lowerCamelCase_		)

							A      :					str      		=					XLMRobertaXLForSequenceClassification(lowerCamelCase_		) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase_		)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							A      :					str      		=					roberta_sent_encoder.embed_tokens.weight
							A      :					List[str]      		=					roberta_sent_encoder.embed_positions.weight
							A      :					str      		=					torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.

							A      :					str      		=					roberta_sent_encoder.layer_norm.weight
							A      :					Any      		=					roberta_sent_encoder.layer_norm.bias

							for i in range(config.num_hidden_layers		):
														# Encoder: start of layer
														A      :					BertLayer      		=					model.roberta.encoder.layer[i]
														A      :					TransformerSentenceEncoderLayer      		=					roberta_sent_encoder.layers[i]

														A      :					RobertaAttention      		=					layer.attention
														A      :					Any      		=					roberta_layer.self_attn_layer_norm.weight
														A      :					Optional[int]      		=					roberta_layer.self_attn_layer_norm.bias

														# self attention
														A      :					BertSelfAttention      		=					layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)		)
														)

														A      :					Tuple      		=					roberta_layer.self_attn.q_proj.weight
														A      :					int      		=					roberta_layer.self_attn.q_proj.bias
														A      :					Tuple      		=					roberta_layer.self_attn.k_proj.weight
														A      :					Dict      		=					roberta_layer.self_attn.k_proj.bias
														A      :					Optional[int]      		=					roberta_layer.self_attn.v_proj.weight
														A      :					Optional[int]      		=					roberta_layer.self_attn.v_proj.bias

														# self-attention output
														A      :					BertSelfOutput      		=					layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														A      :					Optional[Any]      		=					roberta_layer.self_attn.out_proj.weight
														A      :					Union[str, Any]      		=					roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														A      :					Any      		=					roberta_layer.final_layer_norm.weight
														A      :					Any      		=					roberta_layer.final_layer_norm.bias

														# intermediate
														A      :					BertIntermediate      		=					layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														A      :					List[Any]      		=					roberta_layer.fca.weight
														A      :					Dict      		=					roberta_layer.fca.bias

														# output
														A      :					BertOutput      		=					layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														A      :					Optional[int]      		=					roberta_layer.fca.weight
														A      :					Optional[Any]      		=					roberta_layer.fca.bias
														# end of layer

							if classification_head:
														A      :					Any      		=					roberta.model.classification_heads['''mnli'''].dense.weight
														A      :					Optional[Any]      		=					roberta.model.classification_heads['''mnli'''].dense.bias
														A      :					List[str]      		=					roberta.model.classification_heads['''mnli'''].out_proj.weight
														A      :					Union[str, Any]      		=					roberta.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														A      :					Dict      		=					roberta.model.encoder.lm_head.dense.weight
														A      :					str      		=					roberta.model.encoder.lm_head.dense.bias
														A      :					int      		=					roberta.model.encoder.lm_head.layer_norm.weight
														A      :					Optional[Any]      		=					roberta.model.encoder.lm_head.layer_norm.bias
														A      :					Dict      		=					roberta.model.encoder.lm_head.weight
														A      :					Tuple      		=					roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							A      :					torch.Tensor      		=					roberta.encode(lowerCamelCase_		).unsqueeze(0		)  # batch of size 1

							A      :					Optional[int]      		=					model(lowerCamelCase_		)[0]
							if classification_head:
														A      :					Dict      		=					roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCamelCase_		)		)
							else:
														A      :					Optional[int]      		=					roberta.model(lowerCamelCase_		)[0]
							print(our_output.shape     ,				their_output.shape		)
							A      :					Tuple      		=					torch.max(torch.abs(our_output - their_output		)		).item()
							print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
							A      :					int      		=					torch.allclose(lowerCamelCase_     ,				lowerCamelCase_     ,				atol=1E-3		)
							print('''Do both models output the same tensors?'''     ,				'''🔥''' if success else '''💩'''		)
							if not success:
														raise Exception('''Something went wRoNg'''		)

							pathlib.Path(lowerCamelCase_		).mkdir(parents=lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
							print(F'Saving model to {pytorch_dump_folder_path}'		)
							model.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Dict   =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--classification_head", action="store_true", help="Whether to convert a final classification head."
		)
		lowercase			:				List[Any]   =     parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)









import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






